This tutorial comes from Carson Sievert’s Plotly for R Master Class.

Combining Views

Setting up

library(plotly)
library(tidyverse)

p <- txhousing %>% 
  group_by(city) %>%
  plot_ly(x = ~date, y = ~median) %>%
  add_lines(alpha = 0.2, name = "All TX Cities", hoverinfo = "none") %>%
  filter(city == "Houston") %>%
  add_lines(name = "Houston")

p2 <- ggplotly(ggplot(txhousing, aes(date, median)) + geom_bin2d())

With htmlwidgets

Since plotly objects inherit properties from an htmlwidget object, any method that works for arranging htmlwidgets also works for plotly objects. In some sense, an htmlwidget object is just a collection of HTML tags, and the htmltools package provides some useful functions for working with HTML tags (RStudio and Inc. 2016). The tagList() function gathers multiple HTML tags into a tag list, and when printing a tag list inside of a knitr/rmarkdown document (Xie 2016; Allaire et al. 2016), it knows to render as HTML:

library(htmltools)

tagList(p, p2)

This renders two plots, each in its own row spanning the width of the page, because each htmlwidget object is an HTML <div> tag. More often than not, it is desirable to arrange multiple plots in a given row, and there are a few ways to do that. A very flexible approach is to wrap all of your plots in a flexbox (i.e., an HTML <div> with display: flex Cascading Style Sheets (CSS) property). The tags$div() function from htmltools provides a way to wrap a <div> around both tag lists and htmlwidget objects, and set attributes, such as style.

tags$div(
  style = "display: flex; flex-wrap: wrap",
  tags$div(p, style = "width: 45%; padding: 1em;"),
  tags$div(p2, style = "width: 45%; padding: 1em;")
)

With shiny

Another way to arrange multiple htmlwidget objects on a single page is to leverage the fluidPage(), fluidRow(), and column() functions from the shiny package:

library(shiny)

fluidPage(
  fluidRow(
    column(6, p), column(6, p2)
  )
)

With subplot()

We could also use plotly’s built-in subplot() function to generate a single plotly object with a common y-axis

subplot(
  p, p2,
  shareY = TRUE
)

With crosstalk

crosstalk is the R implementation of the powerful crossfilter JS library. Though this dataset isn’t very large, we’ll use it to create a SharedData object that allows us to propagate interaction.

library(crosstalk)

shared_data <- txhousing %>%
  filter(city %in% c("Austin", "Houston", "Dallas")) %>%
  SharedData$new(~year)

As far as ggplotly() and plot_ly() are concerned, SharedData object(s) act just like a data frame, but with a special key attribute attached to graphical elements. Since both interfaces are based on the layered grammar of graphics, key attributes can be attached at the layer level, and those attributes can also be shared across multiple views. Let’s leverage both of these features to link multiple views of median house sales in various Texan cities:

p <- ggplot(shared_data, aes(month, median)) + 
  geom_line(aes(group = year)) + 
  facet_wrap(~ city)

ggplotly(p, tooltip = "year") %>%
  highlight(color = "red") # click on a line to see highlight

We can also link different views, like those from ggpairs. Let’s look at the penguins dataset because it’s easy on the eyes:

library(palmerpenguins)

penguins <- penguins %>% select(species, 
                                bill_length_mm, 
                                bill_depth_mm, 
                                flipper_length_mm, 
                                body_mass_g) %>% 
  drop_na()

shared_penguins <- SharedData$new(penguins)

p <- GGally::ggpairs(shared_penguins, aes(color = species), columns = 2:5)

highlight(ggplotly(p), on = "plotly_selected") # brush to see brushing and linking

Really, we can link any collection of htmlwidgets:

library(leaflet)

shared_quakes <- SharedData$new(quakes)

p <- plot_ly(shared_quakes, x = ~depth, y = ~mag) %>% 
  add_markers(alpha = 0.5) %>%
  highlight("plotly_selected", dynamic = TRUE)

map <- leaflet(shared_quakes) %>% 
  addTiles() %>% 
  addCircles()

bscols(widths = c(6, 6), p, map)

We’ll play more with leaflet maps in our upcoming unit on geograhic data.

Your turn!

Using the palmerspenguins dataset create: 1. A scatterplot showing bill_length_mm by flipper_length_mm, colored by species. 2. A barchart showing counts of penguins across species. 3. Link the two charts such that brushing over points in the scatterplot causes the barchart to show counts only for selected points.